Incremental Feature Subset Selection Algorithm Based on Extension Matrices

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In addition this paper also analyses the methods determining the size of the optimal feature subset and proposes a feature subset size determination algorithm based on training accuracy .

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An algorithm of feature subset selection based on genetic algorithm

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Theoretical and practical computation show that the optimal approximation results can be obtained using the suboptimal subset regression algorithm proposed by the paper for any given precision .

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In order to materialize an appropriate cube subset a heuristic genetic algorithm was proposed .

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Therefore this paper proposes a k-connected and 1-covered node subset construction algorithm & CPC which can construct a node subset that is covering and k-connected with few nodes .

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This paper proposed a criterion function for selecting the optimal feature subset and a search strategy called novel quantum genetic algorithm ( NQGA ) .

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In view of the deficiencies in traditional combination optimization method an algorithm of feature subset selection based on genetic algorithm is proposed in this paper .

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After analyzing problems of the feature subset selection in the field of data mining a new algorithm of feature subset selection based on the improved genetic algorithm is proposed .

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Subset Search algorithm based on Hybrid PSO and GA ( HPSOGA ) for hyperspectral data .

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The basic algorithm for feature subset select which use genetic algorithm and immune genetic algorithm are analyzed . Simulation shows that this algorithm is validity and feasibility .

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Eigenvector Subset Selection Using Bayesian Optimization Algorithm

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The efficiency of algorithm is enhanced by setting up intersection subset of these two in the previous algorithm .

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Therefore the main research direction of attribute reduction focus on the more effective attribute reduction algorithm the optimal feature subset reduce the time complexity algorithm and improve the accuracy of the optimal .

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The simulation results show that the nodes in every subset brought by the new algorithm are uniformly distributed and analysis shows that the new algorithm can work with a very low time complexity and message complexity so it has a great practicability .

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Study on the Image Reconstruction Subset Sort of OSEM Algorithm

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A New Entropy-Based Feature Subset Selection Algorithm

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Control Flow Paths Subset of Tested Program Generation Algorithm Based on LCC

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The failure detection of dynamical system can be considered as the classification between the subset with failure and the subset without failure . So the intelligent decision algorithm for failure detection is derived .

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This model includes a historical database a procedure of searching for the nearest neighbor subset and its optimization algorithm and the technique of predict and estimation .

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On the Complexity of Solving Low-Density Subset Sum Problem with the Lattice Basis Reduction Algorithm

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The simulation results show that the algorithm has preferable robustness and can effectively resist all kinds of subset attacks . 3 . A digital watermarking algorithm for authenticating the database integrity is proposed .

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Given the same feature subset the proposed genetic algorithm utilizes fewer samples and obtains greater accuracy than random sampling .

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At each iteration the C-Step method originated from the MCD estimator is adopted to adjust the subset and to ensure the robustness of the algorithm by ejecting outliers .

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The optimal feature subset which is searched by algorithm provides an important referential value for liver disease .

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This paper by means of set theory presents a modified suboptimal subset polynomial regression algorithm based on abstract algebra . In the paper most of contents for regression process are adequately described and discussed in mathematics one important theorem and corollary are given .

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This method can get the best feature subset by searching the feature set using Quantum Evolution Algorithm .

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Experimental results show that as compared with several traditional algorithms higher image classification accuracies are achieved with the selected subset of features from the proposed algorithm .

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The order of applied projections and the selection of subset in the cone beam iterative algorithm have a great effect on speed of convergence and accuracy in reconstructed images .

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The sub-region subset algorithm is proposed . This algorithm selects LAPLACE edge detection operator seriously affected by noise to separate reconstruction images .

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